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How to Check Column Nulls and Replace: Pandas

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Here is a post that shows how to count Nulls and replace them with the value you want in the Pandas Dataframe. We have explained the process in two steps - Counting and Replacing the Null values. Count null values (column-wise) in Pandas ## count null values column-wise null_counts = df.isnull(). sum() print(null_counts) ``` Output: ``` Column1    1 Column2    1 Column3    5 dtype: int64 ``` In the above code, we first create a sample Pandas DataFrame `df` with some null values. Then, we use the `isnull()` function to create a DataFrame of the same shape as `df`, where each element is a boolean value indicating whether that element is null or not. Finally, we use the `sum()` function to count the number of null values in each column of the resulting DataFrame. The output shows the count of null values column-wise. to count null values column-wise: ``` df.isnull().sum() ``` ##Code snippet to count null values row-wise: ``` df.isnull().sum(axis=1) ``` In the above code, `df` is the Panda
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6 Advantages of Columnar Databases over Traditional RDBMS

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In traditional RDBMS, when a data source is accessed by multi users at single time, then database will go into deadlock state.

One of the advantages of a columnar model is that if two or more users want to use a different subset of columns, they do not have to lock out each other.

(Superior benefits for NoSQL Jobs)
        (Superior benefits for NoSQL Jobs)
This design is made easier because of a disk storage method known as RAID (redundant array of independent disks, originally redundant array of inexpensive disks), which combines multiple disk drives into a logical unit. Data is stored in several patterns called levels that have different amounts of redundancy. The idea of the redundancy is that when one drive fails, the other drives can take over. When a replacement disk drive in put in the array, the data is replicated from the other disks in the array and the system is restored.

The following are the various levels of RAID:

RAID 0 (block-level striping without parity or mirroring) has no (or zero) redundancy. It provides improved performance and additional storage but no fault tolerance. It is a starting point for discussion.

In RAID 1 (mirroring without parity or striping) data is written identically to two drives, thereby producing a mirrored set; the read request is serviced by either of the two drives containing the requested data, whichever one involves the least seek time plus rotational latency.

In RAID 10 (mirroring and striping) data is written in stripes across primary disks that have been mirrored to the secondary disks.

In RAID 2 (bit-level striping with dedicated Hamming-code parity) all disk spindle rotation is synchronized, and data is striped such that each sequential bit is on a different drive. Hamming-code parity is calculated across corresponding bits and stored on at least one parity drive. This theoretical RAID level is not used in practice.

In RAID 3 (byte-level striping with dedicated parity) all disk spindle rotation is synchronized, and data is striped so each sequential byte is on a different drive.

RAID 4 (block-level striping with dedicated parity) is equivalent to RAID 5 except that all parity data is stored on a single drive. In this arrangement, files may be distributed between multiple drives.

RAID 5, RAID 6, and other patterns exist; many of them are marketing terms more than technology. The goal is to provide fault tolerance of drive failures, up to n disk drive failures or removals from the array. This makes larger RAID arrays practical, especially for high-availability systems. While this is nice for database people, we get more benefit from parallelism for queries.

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